# Multilingual Reference Need Assessment System for Wikipedia

arXiv: https://arxiv.org/abs/2603.17146 · HTML: https://mlsanity.com/paper/2603.17146/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Compressing Deep Convolutional Networks using Vector Quantization](https://mlsanity.com/paper/1412.6115/) (2014)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [Fine-grained Sentiment Classification using BERT](https://mlsanity.com/paper/1910.03474/) (2019)
- [Large Language Models Are Zero-Shot Text Classifiers](https://mlsanity.com/paper/2312.01044/) (2023)
- [Gender Bias in BERT -- Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task](https://mlsanity.com/paper/2306.15298/) (2023)
- [Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia's Verifiability](https://mlsanity.com/paper/1902.11116/) (2019)
- [Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms](https://mlsanity.com/paper/2407.04183/) (2024)
- [An Open Multilingual System for Scoring Readability of Wikipedia](https://mlsanity.com/paper/2406.01835/) (2024)
- [Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages](https://mlsanity.com/paper/2404.09764/) (2024)
- [Longitudinal Assessment of Reference Quality on Wikipedia](https://mlsanity.com/paper/2303.05227/) (2023)
- [Polarization and reliability of news sources in Wikipedia](https://mlsanity.com/paper/2210.16065/) (2022)
- [A Comparative Study of Reference Reliability in Multiple Language Editions of Wikipedia](https://mlsanity.com/paper/2309.00196/) (2023)
- [Wikinformetrics: Construction and description of an open Wikipedia knowledge graph dataset for informetric purposes](https://mlsanity.com/paper/2210.13830/) (2022)
- [Wikipedia Citations: Reproducible Citation Extraction from Multilingual Wikipedia](https://mlsanity.com/paper/2406.19291/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages](https://mlsanity.com/paper/2605.31136/) (2026)

## Abstract (arXiv)

Wikipedia is a critical source of information for millions of users across the Web. It serves as a key resource for large language models, search engines, question-answering systems, and other Web-based applications. In Wikipedia, content needs to be verifiable, meaning that readers can check that claims are backed by references to reliable sources. This depends on manual verification by editors, an effective but labor-intensive process, especially given the high volume of daily edits. To address this challenge, we introduce a multilingual machine learning system to assist editors in identifying claims requiring citations. Our approach is tested in 10 language editions of Wikipedia, outperforming existing benchmarks for reference need assessment. We not only consider machine learning evaluation metrics but also system requirements, allowing us to explore the trade-offs between model accuracy and computational efficiency under real-world infrastructure constraints. We deploy our system in production and release data and code to support further research.